Quantum Architecture

Quantum architecture research focuses on designing efficient and robust quantum circuits for near-term quantum computers. Current efforts concentrate on automating circuit design using machine learning techniques, such as reinforcement learning and evolutionary algorithms, often incorporating neural networks (e.g., transformers, graph neural networks) to optimize qubit allocation and gate sequences within modular architectures. These advancements aim to reduce the need for expert-level knowledge in quantum circuit design, improving the performance and scalability of quantum algorithms for various applications, including machine learning and Hamiltonian simulation.